考虑加减速行为不对称的终端区空中交通流跟驰模型

doi:10.16097/j.cnki.1009-6744.2024.06.025

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (6): 286-297.DOI: 10.16097/j.cnki.1009-6744.2024.06.025

考虑加减速行为不对称的终端区空中交通流跟驰模型

李善梅^*1，纪亚宏¹，王超¹，黄宝军¹，王远洲²，赵末¹

1. 中国民航大学，空中交通管理学院，天津300300；2.中国民用航空西南地区空中交通管理局，成都610225

收稿日期:2024-08-16 修回日期:2024-10-18 接受日期:2024-10-29 出版日期:2024-12-25 发布日期:2024-12-18
作者简介:李善梅(1982- )，女，天津人，副教授。
基金资助:
国家自然科学基金(52202404)；天津市应用基础多元投入基金重点 (21JCZDJC00780)；中央高校基本科研业务费专项资金(312202YY02)。

An Aircraft-following Model for Air Traffic Flow in Terminal Airspace Considering Asymmetric Acceleration and Deceleration Behavior

LI Shanmei^*1,JI Yahong¹,WANG Chao¹,HUANG Baojun¹,WANGYuanzhou²,ZHAO Mo¹

1. School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China; 2. CAACSouthwest RegionalAdministration, Chengdu 610225, China

Received:2024-08-16 Revised:2024-10-18 Accepted:2024-10-29 Online:2024-12-25 Published:2024-12-18
Supported by:
National Natural Science Foundation of China (52202404)；Key Program of Tianjin Science and Technology Plan (21JCZDJC00780)Fundamental Research Funds for the Central Universities of Ministry of Education of China (312202YY02)。

摘要/Abstract

摘要： 为精细化描述空中交通流跟驰行为，考虑空中交通加减速跟驰行为的不对称性，本文提出一种基于改进智能驾驶员模型(IntelligentDriverModel, IDM)的空中交通流跟驰模型。采用成都终端区实际航迹数据，筛选出跟驰航迹数据；在此基础上，应用遗传算法对经典跟驰模型进行参数标定，选取出IDM模型的拟合精度最好；进而，考虑加速度、前后机间隔、平均速度差和机型等因素，分别构建加速和减速场景下的改进IDM跟驰模型；最后，采用奈奎斯特图分析改进模型的局部稳定性和渐近稳定性，并对加减速场景下的改进跟驰模型进行仿真实验。结果表明，改进IDM模型的稳定范围增大，且在加速和减速场景下，改进IDM模型所需稳定时间分别仅为原始IDM模型的13.9%和22.2%，头机为不同机型时，模型均表现较强稳定性，且头机为重型机时，所需稳定时间最长，这与实际运行中重型机产生的尾流对后机影响最为严重相一致。

关键词: 航空运输, 空中交通流, 跟驰模型, 稳定性, 智能驾驶员模型, 参数标定

Abstract: To accurately describe the following behavior of air traffic flow, particularly considering the asymmetry of air traffic acceleration and deceleration, an air traffic flow following model based on an improved intelligent driver model (IDM) is proposed. Firstly, using actual flight trajectory data from the Chengdu terminal area, the following trajectory data is selected. The genetic algorithm is used to calibrate the parameters of the different classical following models, ultimately selecting the IDM model due to its superior fitting accuracy. Next, the improved IDM following models specifically for acceleration and deceleration scenarios are established considering factors such as acceleration, inter-vehicle spacing, average speed differences, and aircraft types. To assess the stability of the improved model, the Nyquist plot is used to analyze the local stability and asymptotic stability. Simulation experiments are conducted on the improved following model for acceleration and deceleration scenarios. The results show that the stable range of the improved IDM model is increased. For the acceleration and deceleration scenarios, the stability time of the improved IDM model is only 13.9% and 22.2% of the original IDM model, respectively. The model shows strong stability for different types of leading aircraft, and the leading aircraft of heavy requires the longest stable time, which is consistent with the practical observation that heavy aircraft generate the most severe wake turbulence in actual operations.

Key words: air transportation, air traffic flow, aircraft-following model, stability, intelligent driver model, parameter calibration

中图分类号:

李善梅, 纪亚宏, 王超, 黄宝军, 王远洲, 赵末. 考虑加减速行为不对称的终端区空中交通流跟驰模型[J]. 交通运输系统工程与信息, 2024, 24(6): 286-297.

LI Shanmei, JI Yahong, WANG Chao, HUANG Baojun, WANGYuanzhou, ZHAO Mo. An Aircraft-following Model for Air Traffic Flow in Terminal Airspace Considering Asymmetric Acceleration and Deceleration Behavior[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(6): 286-297.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2024.06.025

http://www.tseit.org.cn/CN/Y2024/V24/I6/286

参考文献

[1]赵嶷飞,王梦琦.空中交通工程学理论内涵与关键科学技术[J]. 航空学报,2022, 43(12): 122-163. [ZHAO Y F, WANG M Q. Important theories and critical scientific technology of air traffic engineering[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(12): 122-163.]

[2] 张洪海,胡勇,杨磊,等.多机场终端区微观交通流建模与仿真分析[J]. 西南交通大学学报, 2015, 50(2): 368-374. [ZHANG H H, HU Y, YANG L, et al. Modeling and simulation analysis of microscopic traffic flow in multi-airport terminal airspace[J]. Journal of Southwest Jiaotong University, 2015, 50(2): 368-374.]

[3]张洪海,汤一文,许炎.TBO模式下终端区进场交通流优化模型与仿真分析[J].航空学报,2020,41(7): 325 338. [ZHANG H H, TANG Y W, XU Y. Optimizing arrival traffic flow in airport terminal airspace under trajectory based operations[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(7): 325-338.]

[4]张兆宁,黎新华,王莉莉.基于飞行跟驰模型的纵向安全间隔计算方法[J].交通运输工程学报,2008(3):73 76. [ZHANG Z N, LI X H, WANG L L. Computational method of longitudinal safety separation based on flight following theory[J]. Journal of Traffic and Transportation Engineering, 2008(3): 73-76.]

[5] 黎新华,张兆宁,王莉莉.基于跟驰稳定的终端区容量评估方法[J]. 系统工程理论与实践,2009,29(2): 173- 179. [LI X H, ZHANG Z N, WANG L L. Terminal area capacity evaluation method based on following stability [J]. Systems Engineering-Theory & Practice, 2009, 29(2): 173-179.]

[6]王莉莉,赵云飞,郭微萌.基于飞联网运行的航空器多速度差跟驰模型[J/OL]. 北京航空航天大学学报: (2023-10-11) [2024-03-20]. https://doi. org/10. 13700/ j. bh. 1001-5965. 2023. 0340. [WANG L L, ZHAO Y F, GUO W M. Aircraft multi velocity difference car following model based on flight networking operation[J/ OL]. Journal of Beijing University of Aeronautics and Astronautics: (2023-10-11) [2024-03-20]. https://doi. org/10. 13700/j. bh. 1001-5965. 2023. 0340.]

[7]汤新民,陆晓娜.基于MPC的随机风场下航空器纵向自主间隔控制[J/OL]. 北京航空航天大学学报, (2023 10-24) [2024-03-20]. https://doi. org/10. 13700/j. bh. 1001-5965. 2023. 0414. [TANG X M, LU X N. Longitudinal autonomous separation control of aircraft in random wind fields based on MPC[J/OL]. Journal of Beijing University of Aeronautics and Astronautics, (2023-10-24)[2024-03-20]. https://doi. org/10. 13700/ j. bh. 1001-5965. 2023. 0340.]

[8]袁立罡.终端区动态交通特征与运行态势研究[D].南京: 南京航空航天大学,2017. [YUAN L G. Research on dynamic traffic characteristics and operation situation of terminal area[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017.]

[9] CHANDLER R. E, HERMAN R, MONTROLL E W. Traffic dynamics: Studies in car following[J]. Operations Research, 1958, 6(2): 165-184.

[10] 王昊, 金城杰.交通流理论及应用[M].北京:人民交通出版社, 2020. [WANG H, JIN C J. Traffic flow theory and application[M]. Beijing: China Communication Press, 2020.]

[11] BANDO M, HASEBE K, NAKAYAMA A, et al. Dynamical model of traffic congestion and numerical simulation[J]. Physical Review E, 1995, 51(2): 1035 1042.

[12] JIANG R, WU Q, ZHU Z. Full velocity difference model for a car-following theory[J]. Physical Review E, 2001, 64(1): 017101.

[13] TREIBER M, HENNECKE A, HELBING D. Congested traffic states in empirical observations and microscopic simulations[J]. Physical Review E, 2000, 62(2): 1805.

[14] 程龙.基于深度强化学习的强制换道场景下智能网联车驾驶决策研究[D]. 北京: 北京交通大学, 2023. [CHENG L. Research on driving decision-making of intelligent connected vehicle in mandatory lane changing scenarios based on deep reinforcement learning[D]. Beijing: Beijing Jiaotong University, 2023.]

[15] 李易, 冯国英,马孜.一种自适应交叉率和变异率的薄膜遗传算法[J].光学与光电技术, 2009, 7(1): 92-96. [LI Y, FENG G Y, MA Z. Adaptive aenetic algorithm with crossing and mutation probability for designing optical thin films[J]. Optics & Optoelectronic Technology, 2009, 7(1): 92-96.]

[16] 王雪松, 孙平,张晓春,等.基于自然驾驶数据的高速公路跟驰模型参数标定[J].中国公路学报,2020,33 (5): 132-142. [WANG X S, SUN P, ZHANG X C, et al. Calibrating car-following models on freeway based on naturalistic driving data[J]. China Journal of Highway and Transport, 2020, 33(5): 132-142.]

[17] RESHEF D N, RESHEF Y A, FINUCANE H K, et al. Detecting novel associations in large datasets[J]. Science, 2011, 334(662): 1518-1524.

[18] 李宏刚, 高哈尔·达吾力,王帅,等.考虑临近车道行人对交通流影响的改进跟驰模型[J].北京航空航天大学学报,2019,45(2): 422-428. [LI H G, DAWULI G, WANG S, et al. An improved car-following model considering effect of pedestrians of adjacent lane on traffic flow[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 422-428.]

[19] 宗芳, 王猛, 曾梦,等.考虑多前车作用势的混行交通流车辆跟驰模型[J]. 交通运输工程学报,2022,22(1): 250-262. [ZONG F, WANG M, ZENG M, et al. Vehicle following model in mixed traffic flow considering interaction potential of multiple front vehicles[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 250-262.]

考虑加减速行为不对称的终端区空中交通流跟驰模型

An Aircraft-following Model for Air Traffic Flow in Terminal Airspace Considering Asymmetric Acceleration and Deceleration Behavior

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	邬岚, 周佳雨, 李根. 技术标准更新视角下电动自行车事故严重程度影响因素演化分析[J]. 交通运输系统工程与信息, 2025, 25(1): 231-240.
[2]	张轮, 侯雪晶, 吴凌霄, 徐博. 考虑效率和公平的飞机滑行路径双目标规划研究[J]. 交通运输系统工程与信息, 2025, 25(1): 250-257.
[3]	温瑞英, 何家兴, 王红勇. 支持稳定航迹优化的空中交通多元复杂度计算方法[J]. 交通运输系统工程与信息, 2025, 25(1): 258-269.
[4]	杜鹏. 新时期我国交通运输业碳减排的任务与对策[J]. 交通运输系统工程与信息, 2024, 24(6): 1-4.
[5]	杜文举, 赵尚飞, 李引珍, 张建刚. 考虑前后多车的混合交通流稳定性与安全性分析[J]. 交通运输系统工程与信息, 2024, 24(6): 206-218.
[6]	周航, 赵风旸, 胡小兵. 城市空中交通动态空域垂直起降飞行器路径优化[J]. 交通运输系统工程与信息, 2024, 24(5): 295-308.
[7]	陈丹, 汤程, 张昊, 马园园, 徐凤. 复杂航路网络航空排放精细建模与绿色轨迹优化研究[J]. 交通运输系统工程与信息, 2024, 24(5): 318-326.
[8]	黄岩, 李海军, 闫学东, 段克. 雾天网联车辆跟驰模型构建及行为影响分析[J]. 交通运输系统工程与信息, 2024, 24(4): 41-49.
[9]	张宝成, 胡威, 刘婉纯. 考虑航空公司偏好的协同航路分配优化方法[J]. 交通运输系统工程与信息, 2024, 24(4): 243-252.
[10]	张洪海, 瞿昕宜, 沈雪, 万俊强. 基于传递熵的空中交通航路网络拥堵传播特性分析[J]. 交通运输系统工程与信息, 2024, 24(4): 263-273.
[11]	帅斌, 罗佳楠, 冯心妍, 黄文成. 虚拟编组下基于跟驰模型的列车群运行控制方法研究[J]. 交通运输系统工程与信息, 2024, 24(3): 1-11.
[12]	李明捷, 王涛, 黄欣宁, 田杰, 姚霖昊. 考虑延误特征的航站楼离港聚集客流预测方法[J]. 交通运输系统工程与信息, 2024, 24(3): 240-254.
[13]	周悦, 蒋维安, 付川云, 魏麟, 犹轶. 考虑因素异质影响的直升机事故严重性分析[J]. 交通运输系统工程与信息, 2024, 24(2): 323-332.
[14]	曲大义, 孟奕名, 王韬, 宋慧, 陈意成. 基于分子力场的网联自主车辆跟驰安全特性及模型[J]. 交通运输系统工程与信息, 2023, 23(6): 33-41.
[15]	李善梅, 王雨鑫, 雷青蕾, 宋思霓, 王超. 终端区交通拥堵识别及其时空演化特性分析[J]. 交通运输系统工程与信息, 2023, 23(6): 120-132.